R help - Jan 2013 - Tukey HSD plot with lines indicating (non-)significance

Dear list members,

I'm running some tests looking at differences between means for various 
levels of a factor, using Tukey's HSD method.

I would like to plot the data as boxplots or dotplots, with horizontal 
significance lines indicating which groups are statistically 
significantly different, according to Tukey HSD. Here's a nice image 
showing an example of such a graphical display:

   http://www.biomedcentral.com/1471-2148/11/99/figure/F2

Is there a R function for this? I have tried searching, and found 
several nice plotting fuctions for Tukey's HSD, but they all either hide 
the actual data, or display the results only with letters indicating 
(non-)signifiance.

Examples:

# Fit an one-way ANOVA.
library(DAAG)
l=aov(ShootDryMass ~ trt, data=rice)
summary(l)


# Calculate the Tukey HSD tests and CIs.
# The plot shows all 6*5/2 = 15 confidence intervals,
# but not the data or the actual group means.
l.hsd=TukeyHSD(l, "trt", ordered = TRUE)
l.hsd
plot(l.hsd)


# I rather like this one. It shows the group means in a nice way,
# but hides the data. And checking which groups are different
# isn't always easy (e.g., for NH4Cl - NH4NO3), as one
# has to compare differences to a non-aligned scale (bar).
onewayPlot(l)


# One function that gives a results closer to what I want,
# is the compact letter display (CLD) of the multcomp package.
# This shows boxplots (but it is easy to overlay the actual
# observations, using the points() function) and uses letters
# to indicate non-significance.
#
# (Note that (if I have understood everything correctly) this
# doesn't actually calculcate Tukey HSD values (the "Tukey"
# in the glht() call only selects Tukey *contrasts*), but
# the results should be *very* similar.)
library(multcomp)
l.glht=glht(l, linfct = mcp(trt = "Tukey"))
summary(l.glht)
l.cld=cld(l.glht)
old.par <- par( mai=c(1,1,2,1))
plot(l.cld)
par(old.par)


# Basically, I want a similar display as the one above (or
# preferably a ggplot2- or lattice-based one), but with lines
# instead of letters. Of course, for this, the levels need to be
# reordered by group means, and it is only guaranteed to
# work for balanced data. Example, with the lines missing:
library(ggplot2)
d=rice
d$trt=reorder(d$trt, -d$ShootDryMass)
ggplot(d, aes(x=trt, y=ShootDryMass)) + geom_boxplot(outlier.colour=NA) 
+
   geom_jitter(col="red", size=3, position=position_jitter(width=.1))

Is there such a function available in R?

-- 
Karl Ove Hufthammer

Please look at the MMC (Mean-mean Multiple Comparisons) plot in the HH
package.
It displays both the means and the differences.

install.packages("HH") ## if you don't already have it.
library(HH)
?MMC

Rich


On Mon, Jan 14, 2013 at 12:36 PM, Karl Ove Hufthammer
<karl@huftis.org>wrote:
> Dear list members,
>
> I'm running some tests looking at differences between means for various
> levels of a factor, using Tukey's HSD method.
>
> I would like to plot the data as boxplots or dotplots, with horizontal
> significance lines indicating which groups are statistically significantly
> different, according to Tukey HSD. Here's a nice image showing an
example
> of such a graphical display:
>
>  
http://www.biomedcentral.com/**1471-2148/11/99/figure/F2<http://www.biomedcentral.com/1471-2148/11/99/figure/F2>
>
> Is there a R function for this? I have tried searching, and found several
> nice plotting fuctions for Tukey's HSD, but they all either hide the
actual
> data, or display the results only with letters indicating
(non-)signifiance.
>
> Examples:
>
> # Fit an one-way ANOVA.
> library(DAAG)
> l=aov(ShootDryMass ~ trt, data=rice)
> summary(l)
>
>
> # Calculate the Tukey HSD tests and CIs.
> # The plot shows all 6*5/2 = 15 confidence intervals,
> # but not the data or the actual group means.
> l.hsd=TukeyHSD(l, "trt", ordered = TRUE)
> l.hsd
> plot(l.hsd)
>
>
> # I rather like this one. It shows the group means in a nice way,
> # but hides the data. And checking which groups are different
> # isn't always easy (e.g., for NH4Cl - NH4NO3), as one
> # has to compare differences to a non-aligned scale (bar).
> onewayPlot(l)
>
>
> # One function that gives a results closer to what I want,
> # is the compact letter display (CLD) of the multcomp package.
> # This shows boxplots (but it is easy to overlay the actual
> # observations, using the points() function) and uses letters
> # to indicate non-significance.
> #
> # (Note that (if I have understood everything correctly) this
> # doesn't actually calculcate Tukey HSD values (the "Tukey"
> # in the glht() call only selects Tukey *contrasts*), but
> # the results should be *very* similar.)
> library(multcomp)
> l.glht=glht(l, linfct = mcp(trt = "Tukey"))
> summary(l.glht)
> l.cld=cld(l.glht)
> old.par <- par( mai=c(1,1,2,1))
> plot(l.cld)
> par(old.par)
>
>
> # Basically, I want a similar display as the one above (or
> # preferably a ggplot2- or lattice-based one), but with lines
> # instead of letters. Of course, for this, the levels need to be
> # reordered by group means, and it is only guaranteed to
> # work for balanced data. Example, with the lines missing:
> library(ggplot2)
> d=rice
> d$trt=reorder(d$trt, -d$ShootDryMass)
> ggplot(d, aes(x=trt, y=ShootDryMass)) + geom_boxplot(outlier.colour=**NA)
> +
>   geom_jitter(col="red", size=3,
position=position_jitter(**width=.1))
>
> Is there such a function available in R?
>
> --
> Karl Ove Hufthammer
>
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